docstr: Create the module settings
  
  create_settings is called at the end of initialization.
docstr_tok: ["Create","the","module","settings","create_settings","is","called","at","the","end","of","initialization","."]
code: def create_settings(self):
      """Create the module settings
  
      create_settings is called at the end of initialization.
      """
      self.object = LabelSubscriber(
          text="Select the object whose measurements will be displayed",
          value="None",
          doc=textwrap.dedent(
              """\
                  Choose the name of objects identified by some previous module (such as
                  **IdentifyPrimaryObjects** or **IdentifySecondaryObjects**) whose
                  measurements are to be displayed.
              """
          ),
      )
  
      self.x_axis = Measurement(
          text="Select the object measurement to plot",
          object_fn=self.get_object,
          value="None",
          doc="Choose the object measurement made by a previous module to plot.",
      )
  
      self.bins = Integer(
          text="Number of bins",
          value=100,
          minval=1,
          maxval=1000,
          doc="Enter the number of equally-spaced bins that you want used on the X-axis.",
      )
  
      self.xscale = Choice(
          text="How should the X-axis be scaled?",
          choices=["linear", "log"],
          value=None,
          doc=textwrap.dedent(
              """\
                  The measurement data can be scaled with either a **{LINEAR}** scale or
                  a **{LOG_NATURAL}** (natural log) scaling.
  
                  Log scaling is useful when one of the measurements being plotted covers
                  a large range of values; a log scale can bring out features in the
                  measurements that would not easily be seen if the measurement is plotted
                  linearly.
                  """.format(
                  LINEAR="linear",
                  LOG_NATURAL="log",
              )
          ),
      )
  
      self.yscale = Choice(
          text="How should the Y-axis be scaled?",
          choices=["linear", "log"],
          value=None,
          doc=textwrap.dedent(
              """\
                  The Y-axis can be scaled either with either a **{LINEAR}** scale or a **{LOG_NATURAL}**
                  (natural log) scaling.
  
                  Log scaling is useful when one of the measurements being plotted covers
                  a large range of values; a log scale can bring out features in the
                  measurements that would not easily be seen if the measurement is plotted
                  linearly.
                  """.format(
                  LINEAR="linear",
                  LOG_NATURAL="log",
              )
          ),
      )
  
      self.title = Text(
          text="Enter a title for the plot, if desired",
          value="",
          doc=textwrap.dedent(
              """\
                  Enter a title for the plot. If you leave this blank, the title will
                  default to *(cycle N)* where *N* is the current image cycle being
                  executed.
                  """
          ),
      )
  
      self.wants_xbounds = Binary(
          text="Specify min/max bounds for the X-axis?",
          value=False,
          doc=textwrap.dedent(
              """\
                  Select "**{YES}**" to specify minimum and maximum values for the plot on
                  the X-axis. This is helpful if an outlier bin skews the plot such that
                  the bins of interest are no longer visible.
                  """.format(
                  YES="Yes"
              )
          ),
      )
  
      self.xbounds = FloatRange(
          text="Minimum/maximum values for the X-axis",
          doc="Set lower/upper limits for X-axis of the histogram.",
      )
  
file_path: CellProfiler/src/frontend/cellprofiler/modules/displayhistogram.py
imports: import textwrap
  from cellprofiler_core.module import Module
  from cellprofiler_core.setting import Binary
  from cellprofiler_core.setting import Measurement
  from cellprofiler_core.setting.choice import Choice
  from cellprofiler_core.setting.range import FloatRange
  from cellprofiler_core.setting.subscriber import LabelSubscriber
  from cellprofiler_core.setting.text import Integer
  from cellprofiler_core.setting.text import Text
label_desc: Use image node_id 2 for calling the DisplayHistogram obj's underlying member method code with example usage: obj.create_settings() without return types